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convert : more consistent handling of rope_parameters (#24833)

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Sigbjørn Skjæret
2026-06-20 13:42:36 +03:00
committed by GitHub
parent f449e05537
commit f4043fec01
15 changed files with 40 additions and 39 deletions
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conversion/nemotron.py
+4 -3
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@@ -125,17 +125,18 @@ class NemotronModel(TextModel):
self.gguf_writer.add_layer_norm_eps(f_norm_eps)
# * Partial RoPE
rot_pct = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"])
rot_pct = self.rope_parameters["partial_rotary_factor"]
n_embd = self.find_hparam(["hidden_size", "n_embd"])
n_head = self.find_hparam(["num_attention_heads", "n_head"])
self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
# * RopeScaling for Nemotron
if "rope_scaling" not in self.hparams or self.hparams["rope_scaling"] is None:
factor = self.hparams.get("factor") or self.rope_parameters.get("factor")
if factor is None:
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
else:
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["factor"])
self.gguf_writer.add_rope_scaling_factor(factor)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# * Adding +1 to LayerNorm's weights here to implement layernorm1p w/o changing anything on the GGML engine side